A Method for Traffic Flow Forecasting in a Large-Scale Road Network Using Multifeatures
| Autor: | Zhou Chuhao, Peiqun Lin, Lin Xukun, Yang Cheng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
business.industry Computer science Deep learning traffic flow prediction deep learning multistep prediction toll station management Ocean Engineering computer.software_genre Traffic flow Backpropagation Robustness (computer science) Benchmark (computing) Artificial intelligence Data mining Scale (map) business Engineering (miscellaneous) computer Predictive modelling Civil and Structural Engineering |
| Zdroj: | Promet-Traffic&Transportation Volume 33 Issue 4 |
| ISSN: | 1848-4069 0353-5320 |
| Popis: | Accurate traffic prediction on a large-scale road network is significant for traffic operations and management. In this study, we propose an equation for achieving a comprehensive and accurate prediction that effectively combines traffic data and non-traffic data. Based on that, we developed a novel prediction model, called the adaptive deep neural network (ADNN). In the ADNN, we use two long short-term memory (LSTM) networks to extract spatial-temporal characteristics and temporal characteristics, respectively. A backpropagation neural network (BPNN) is also employed to represent situations from contextual factors such as station index, forecast horizon, and weather. The experimental results show that the prediction of ADNN for different stations and different forecast horizons has high accuracy; even for one hour ahead, its performance is also satisfactory. The comparison of ADNN and several benchmark prediction models also indicates the robustness of the ADNN. |
| Databáze: | OpenAIRE |
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